SentinelPoT is most valuable where an AI or agent can move money, change records, alter infrastructure, or act on a person — and where the organization must later prove that each action was checked. Six domains where that is already true, and what the gate does in each.
Banks and asset managers are giving agents authority that used to require a second signature: releasing a wire, executing an order, changing account tiers, issuing refunds. The controls that governed the human — dual control, limits, pre-trade checks — do not automatically transfer to the agent, and an agent that follows an injected instruction executes at machine speed.
SentinelPoT sits between the agent and the rails. Every transfer, order or record change is validated against the desk's policy before it executes, escalated to a named approver when it exceeds a limit or its provenance is doubtful, and attested either way.
See the treasury transfer and equity trade scenarios in the console →Autonomous treasury agents, AI-controlled wallets, DeFi strategy agents, oracle and bridge interactions, signing workflows, agent-generated transactions and AI-generated contract logic: every one of these turns a model's output into an irreversible on-chain state change. There is no chargeback, no business day, and no supervisor between the agent and the signature unless something is put there.
SentinelPoT's signing hook runs before a transaction is signed. Calldata is simulated and checked for drain patterns such as unlimited approvals to unknown spenders; destinations are checked against allow-lists; amounts against caps; the signing policy (for example 2-of-3) is enforced; and the attestation records the transaction, the checks and the disposition so that the treasury's controls can be shown to a board or an auditor.
See the smart-contract transaction scenario in the console →A token that represents equity, fund interests, private credit or real estate is a security recorded on new infrastructure — U.S. regulators have been explicit that tokenization changes the ledger, not the classification. What is new is the operating surface: the mint, burn, distribution, transfer-restriction and contract-upgrade actions that used to be spread across a transfer agent, a fund administrator and a custodian now execute as transactions, increasingly initiated by software agents. Each one is a consequential action, and each one will need to be shown to a regulator, an auditor or a board.
SentinelPoT gates those operations the way it gates a wire or a database write. Every issuance, distribution, allow-list change and upgrade is validated against the offering's own rules before it is signed — authorized supply, eligible holders, approved distribution schedule, code hash matching the reviewed build, required approvals — escalated when it exceeds a tier, and attested in a hash-chained log the issuer's auditor can verify without trusting the issuer or the platform. It is continuous control evidence for token operations, not an opinion on the underlying asset.
We do not attest to asset ownership, valuation or reserves; those are audit opinions and belong with the firms licensed to give them. We supply the evidence that the operations on the token ran under control, which is the part those firms cannot see today. Nothing here is a claim of compliance with pending legislation.
See the tokenized-asset operations scenarios in the console →Agents with write access to databases, internal APIs, CI/CD and cloud infrastructure are the fastest-growing consequential-action surface in the enterprise. The risks are specific: a Model Context Protocol server whose tool descriptions drift after pinning; a tool called outside its approved manifest; a support ticket carrying an instruction the agent obeys; a Terraform plan that destroys a production database; a multi-agent workflow in which one agent's output becomes another's authority.
SentinelPoT deploys as an MCP gateway or an SDK wrapper around tool functions. Every invocation is validated against a scoped authorization manifest with argument-level policy — permissions live in the parameters, not just the tool — with human approval gates for high-risk combinations, memory-integrity checks so persisted instructions cannot outrank operator policy, and trust weighting across agents in a workflow.
See the database write, MCP tool call and infrastructure change scenarios →Two problems share one shape. An automated recommendation about a person — a promotion board, a benefits determination — must be fair, explainable and traceable before it becomes a system-of-record decision. A query against surveillance data — a plate-reader network, a records system — must be case-linked, proportionate and within a use policy the public agreed to. In both, the failure is discovered by audit long after it happened.
SentinelPoT enforces the jurisdiction's own policy at the moment of the decision or the query: protected attributes masked, rationale completeness and disparate-impact tests on recommendations; case linkage, scope limits, sharing agreements and sensitive-location exclusions on queries. The attestation log yields a public transparency report whose figures can be verified without trusting the vendor or the department.
See the plate-reader and promotion-board consoles → Read the technical report on verifiable use policies →Construction draws, insurance claims and milestone-based contracts release money on the strength of photographs and video. Generative models make convincing evidence cheap. The consequential action is not the upload — it is the payment that follows it — and that is the action SentinelPoT gates.
Proof is captured in a live workflow with time, location and motion signals; verified for capture integrity, temporal and physical plausibility, synthetic artifacts and cross-modal consistency; and scored against the vendor's history. The payment release executes only on an approved proof, and a signed receipt records what was examined, whether it passed, and the paths to release when it did not.
See the physical-proof console →Every domain above is delivered the same way: we red-team one agent or model, instrument its highest-risk actions with Sentinel Runtime in monitor mode, then switch on enforcement tier by tier, and hand you an attestation log you can verify independently.